
"""simple docstring"""


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







       """simple docstring"""
       return 1.0 / (1.0 + np.exp(-_outputs					))



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		np.max(_outputs		,   axis=-1		,   keepdims=UpperCAmelCase_					)
       UpperCamelCase       			=		np.exp(_outputs - maxes					)
       return shifted_exp / shifted_exp.sum(axis=-1		,   keepdims=UpperCAmelCase_					)



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      '''sigmoid'''
       UpperCamelCase_		:       Any				=      '''softmax'''
       UpperCamelCase_		:       Union[str, Any]				=      '''none'''





@add_end_docstrings(
    _lowerCAmelCase     ,  R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''     ,  )
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       int				=      False
       UpperCamelCase_		:       Dict				=      ClassificationFunction.NONE






       def __init__(	self			:				Optional[int]							,   **UpperCAmelCase_			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              self.check_model_type(
                  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
                  if self.framework == "tf"
                  else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				Optional[Any]=None							,   UpperCAmelCase_			:				List[Any]=None							,   UpperCAmelCase_			:				int=""							,   **UpperCAmelCase_			:				List[Any]      )->	Any:







              """simple docstring"""
              # Using "" as default argument because we're going to use `top_k=None` in user code to declare
              # "No top_k"
              UpperCamelCase       			=		tokenizer_kwargs

              UpperCamelCase       			=		{}
              if hasattr(self.model.config							,   "return_all_scores"      ) and return_all_scores is None:
                     UpperCamelCase       			=		self.model.config.return_all_scores

              if isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ) or top_k is None:
                     UpperCamelCase       			=		top_k
                     UpperCamelCase       			=		False
              elif return_all_scores is not None:
                     warnings.warn(
                         "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
                         " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."							,   UpperCAmelCase_							,   )
                     if return_all_scores:
                            UpperCamelCase       			=		None
                     else:
                            UpperCamelCase       			=		1

              if isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		ClassificationFunction[function_to_apply.upper()]

              if function_to_apply is not None:
                     UpperCamelCase       			=		function_to_apply
              return preprocess_params, {}, postprocess_params






       def __call__(	self			:				Tuple							,   *UpperCAmelCase_			:				Union[str, Any]							,   **UpperCAmelCase_			:				Dict      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		super().__call__(*UpperCAmelCase_							,   **UpperCAmelCase_      )
              # TODO try and retrieve it in a nicer way from _sanitize_parameters.
              UpperCamelCase       			=		"top_k" not in kwargs
              if isinstance(args[0]							,   UpperCAmelCase_      ) and _legacy:
                     # This pipeline is odd, and return a list when single item is run
                     return [result]
              else:
                     return result






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				int							,   **UpperCAmelCase_			:				Union[str, Any]      )->	Dict[str, GenericTensor]:







              """simple docstring"""
              UpperCamelCase       			=		self.framework
              if isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     return self.tokenizer(**UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_      )
              elif isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ) and len(UpperCAmelCase_      ) == 1 and isinstance(inputs[0]							,   UpperCAmelCase_      ) and len(inputs[0]      ) == 2:
                     # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                     return self.tokenizer(
                         text=inputs[0][0]							,   text_pair=inputs[0][1]							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_      )
              elif isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                     raise ValueError(
                         "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
                         " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."      )
              return self.tokenizer(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				Dict      )->	int:







              """simple docstring"""
              return self.model(**UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Union[str, Any]=None							,   UpperCAmelCase_			:				str=1							,   UpperCAmelCase_			:				Union[str, Any]=True      )->	List[str]:







              """simple docstring"""
              # `_legacy` is used to determine if we're running the naked pipeline and in backward
              # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
              # the more natural result containing the list.
              # Default value before `set_parameters`
              if function_to_apply is None:
                     if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                            UpperCamelCase       			=		ClassificationFunction.SIGMOID
                     elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                            UpperCamelCase       			=		ClassificationFunction.SOFTMAX
                     elif hasattr(self.model.config							,   "function_to_apply"      ) and function_to_apply is None:
                            UpperCamelCase       			=		self.model.config.function_to_apply
                     else:
                            UpperCamelCase       			=		ClassificationFunction.NONE

              UpperCamelCase       			=		model_outputs["logits"][0]
              UpperCamelCase       			=		outputs.numpy()

              if function_to_apply == ClassificationFunction.SIGMOID:
                     UpperCamelCase       			=		sigmoid(UpperCAmelCase_      )
              elif function_to_apply == ClassificationFunction.SOFTMAX:
                     UpperCamelCase       			=		softmax(UpperCAmelCase_      )
              elif function_to_apply == ClassificationFunction.NONE:
                     UpperCamelCase       			=		outputs
              else:
                     raise ValueError(f"Unrecognized `function_to_apply` argument: {function_to_apply}"      )

              if top_k == 1 and _legacy:
                     return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

              UpperCamelCase       			=		[
                  {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(UpperCAmelCase_      )
              ]
              if not _legacy:
                     dict_scores.sort(key=lambda UpperCAmelCase_      : x["score"]							,   reverse=UpperCAmelCase_      )
                     if top_k is not None:
                            UpperCamelCase       			=		dict_scores[:top_k]
              return dict_scores







"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		name
              UpperCamelCase       			=		value
              UpperCamelCase       			=		weight






       def __repr__(	self			:				int      )->	Any:







              """simple docstring"""
              return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              return self.value






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              return self.name






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              return self.weight






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              return self.value / self.weight





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(UpperCAmelCase_					)					):
              menu.append(Things(name[i]		,   value[i]		,   weight[i]					)					)
       return menu



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		sorted(UpperCAmelCase_		,   key=UpperCAmelCase_		,   reverse=UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase			,							UpperCamelCase       			=		0.0, 0.0
       for i in range(len(UpperCAmelCase_					)					):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]					)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)



def 						lowerCamelCase__      (				)->				Dict:







       """simple docstring"""


if __name__ == "__main__":
      import doctest

      doctest.testmod()





